The first version of an AI-powered workflow is rarely the hard part. Keeping it accurate once it’s live, as inputs drift and edge cases show up, is where most of the real engineering work happens.
Guardrails that catch problems before they reach a customer
Confidence thresholds, structured output validation, and a clear fallback path for low-confidence results all matter more than picking the fanciest model. A system that reliably says “I’m not sure, escalate this” beats one that occasionally answers confidently and wrong.
Evaluation and human review as an ongoing habit
Treat evaluation as a recurring task, not a one-time launch checklist. Sampling a portion of automated decisions for human review, tracking where the system disagrees with a person, and feeding that back into prompts or rules keeps quality from quietly drifting over time.
None of this is exotic. It’s closer to the discipline teams already apply to any system that makes decisions on their behalf, just applied to a newer kind of tool.
